Teradata Package for R Function Reference | 17.20 - SVMSparsePredict - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

SparseSVMPredictor

Description

The SparseSVMPredictor function takes the model generated by the function SparseSVMTrainer (td_svm_sparse_mle) and a set of test samples (in sparse format) and outputs a prediction for each sample.

Usage

  td_svm_sparse_predict_mle_sqle (
      object = NULL,
      newdata = NULL,
      sample.id.column = NULL,
      attribute.column = NULL,
      value.column = NULL,
      accumulate.label = NULL,
      output.class.num = 1,
      newdata.partition.column = NULL,
      newdata.order.column = NULL,
      object.order.column = NULL
  )
## S3 method for class 'td_svm_sparse_mle'
predict(
      object = NULL,
      newdata = NULL,
      sample.id.column = NULL,
      attribute.column = NULL,
      value.column = NULL,
      accumulate.label = NULL,
      output.class.num = 1,
      newdata.partition.column = NULL,
      newdata.order.column = NULL,
      object.order.column = NULL)

Arguments

object	Required Argument. Specifies the model tbl_teradata generated by td_svm_sparse_mle. This argument can accept either a tbl_teradata or an object of "td_svm_sparse_mle" class.
object.order.column	Optional Argument. Specifies Order By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
newdata	Required Argument. Specifies the tbl_teradata containing the input test data.
newdata.partition.column	Required Argument. Specifies Partition By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
newdata.order.column	Optional Argument. Specifies Order By columns for "newdata". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
sample.id.column	Required Argument. Specifies the name of the "newdata" column that contains the identifiers of the test samples. The "newdata" tbl_teradata must be partitioned by this column. Types: character
attribute.column	Required Argument. Specifies the name of the "newdata" column that contains the attributes of the test samples. Types: character
value.column	Optional Argument. Specifies the name of the "newdata" column that contains the attribute values. By default, each attribute has the value 1. Types: character
accumulate.label	Optional Argument. Specifies the names of the "newdata" columns to copy to the output tbl_teradata. Types: character OR vector of Strings (character)
output.class.num	Optional Argument. Specifies the number of class labels to appear in the output tbl_teradata, with its corresponding prediction confidence. Note: This argument is valid only for multiple-class models. Default Value: 1 Types: integer

Value

Function returns an object of class "td_svm_sparse_predict_mle_sqle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using the name: result.

Examples

  
    # Get the current context/connection.
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("svmsparsepredict_example", "svm_iris_input_test",
                    "svm_iris_input_train")
    
    # Create object(s) of class "tbl_teradata".
    svm_iris_input_train <- tbl(con, "svm_iris_input_train")
    svm_iris_input_test <- tbl(con, "svm_iris_input_test")
    
    # Example - 
    # Create the Sparse SVM model.
    svm_train <- td_svm_sparse_mle(data = svm_iris_input_train, 
                                   sample.id.column = 'id', 
                                   attribute.column = 'attribute', 
                                   value.column = 'value1', 
                                   label.column = 'species', 
                                   max.step = 150, 
                                   seed = 0
                                   )
    
    # Run predict on the output of td_svm_sparse_mle() function.
    svm_sparse_predict_result <- td_svm_sparse_predict_mle_sqle(
                                  newdata = svm_iris_input_test,
                                  newdata.partition.column = c("id"),
                                  object = svm_train,
                                  sample.id.column = "id",
                                  attribute.column = "attribute",
                                  value.column = "value1",
                                  accumulate.label = c("species")
                                  )
                                    
    # Alternatively use S3 predict on the output of td_svm_sparse_mle() to 
    # find prediction.
    predict_out <- predict(svm_train,
                           newdata = svm_iris_input_test,
                           newdata.partition.column = c("id"),
                           sample.id.column = "id",
                           attribute.column = "attribute",
                           value.column = "value1",
                           accumulate.label = c("species")
                           )